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Found 38 papers, 13 papers with code
Streaming Probabilistic PCA for Missing Data with Heteroscedastic Noise
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces of very high dimensional and high arrival-rate data with missing entries and corrupting noise.
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Other methods such as Weighted PCA (WPCA) assume the noise variances are known, which may be difficult to know in practice.
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior
Phase retrieval (PR) is a crucial problem in many imaging applications.
Dynamic Subspace Estimation with Grassmannian Geodesics
We propose a novel algorithm for minimizing this objective and estimating the parameters of the model from data with Grassmannian-constrained optimization.
Adaptive Sampling for Linear Sensing Systems via Langevin Dynamics
Adaptive or dynamic signal sampling in sensing systems can adapt subsequent sampling strategies based on acquired signals, thereby potentially improving image quality and speed.
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA).
Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)
Optimizing 3D k-space sampling trajectories for efficient MRI is important yet challenging.
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
This work focuses on image reconstruction in such settings, i. e., when both the number of available CT projections and the training data is extremely limited.
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
In fact, we show that model-based image reconstruction (MBIR) methods with suitably optimized imaging parameters can perform nearly as well as CNN-based methods.
Manifold Model for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification
Oscillating Steady-State Imaging (OSSI) is a recent fMRI acquisition method that exploits a large and oscillating signal, and can provide high SNR fMRI.
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction
We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.
B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.
Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI
This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses.
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net.
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision.
Improved low-count quantitative PET reconstruction with an iterative neural network
Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR).
Optimizing MRF-ASL Scan Design for Precise Quantification of Brain Hemodynamics using Neural Network Regression
We also propose a regression based estimation framework for MRF-ASL.
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning
In vivo and ex vivo experiments demonstrate that MESE MWF and DESS PERK ff estimates are quantitatively comparable measures of WM myelin water content.
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Signal Processing Image and Video Processing Optimization and Control Medical Physics
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging.
Convolutional Analysis Operator Learning: Acceleration and Convergence
This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems.
Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK).
Medical image reconstruction: a brief overview of past milestones and future directions
This paper briefly reviews past milestones in the field of medical image reconstruction and describes some future directions.
Medical Physics
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.
Convolutional Dictionary Learning: Acceleration and Convergence
However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems.
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements.
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples.
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction
Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The $\ell_0$ Method
The proposed block coordinate descent algorithm involves efficient closed-form solutions.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns.
A convergence proof of the split Bregman method for regularized least-squares problems
According to our analysis, we can show that the two-split ADMM algorithm can be faster than the SB method if the AL penalty parameter of the SB method is suboptimal.
Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions.
